For economic and humanitarian reasons, it is desirable to decrease the number of traffic collisions between multiple vehicles, between vehicles and stationary objects, and between vehicles and moving objects such as humans. Accordingly, the development of image processing systems for vehicles that enable detection of an impending collision is desirable. In addition, the development of such image processing systems that are able to determine the context of an image, for example whether the object is a vehicle, stationary object, or person, is even more desirable since such a system can be used in a collision avoidance system installed in a vehicle.
Such collision avoidance systems can actuate the brakes or steering of a car, for example, so as to avoid an impending collision. The contextual information can be used to alter the brake or steering actuation of the car so as to help minimize economic loss, or so as to help avoid injury to a person during a collision between the vehicle and the person. One successful way to derive this contextual information from an image is to generate histogram of oriented gradient (HOG) descriptors from that image, and to analyze the HOG descriptors to derive the contextual information.
A variety of techniques used for extracting HOG descriptors exist. For example, two leading techniques are the Dalal-Triggs technique, and the Felzenszwalb technique. While these techniques are useful, the resulting data generated is voluminous. This can be a disadvantage in image processing systems utilizing multiple processors, as the bandwidth used to send this data between the multiple processors may not be available.
Therefore, further advances in the field of image processing so as to enable the extraction of HOG descriptors and the transmission of the HOG descriptors without the use of excessive bandwidth are desired.